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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 101–110 of 2050 papers

TitleStatusHype
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
Can We Characterize Tasks Without Labels or Features?Code1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Extended Stochastic Block Models with Application to Criminal NetworksCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
Foundation Model is Efficient Multimodal Multitask Model SelectorCode1
Fuzzy c-Means Clustering for Persistence DiagramsCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
Automated Machine Learning in InsuranceCode1
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